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Interference Effects of Weed-Infested Bands in or Between Crop Rows on Field Corn (Zea mays) Yield

Published online by Cambridge University Press:  20 January 2017

William W. Donald  and
William G. Johnson
William W. Donald*
Affiliation:
United States Department of Agriculture, Agricultural Research Service, 269 Agricultural Engineering Building
William G. Johnson
Affiliation:
204 Waters Hall, Department of Agronomy, University of Missouri, Columbia, MO 65211
*
Corresponding author's E-mail: donaldw@missouri.edu
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Abstract

The effect of season-long interference by bands of weeds growing only between rows (BR) on field corn yields has not been reported before or compared with weedy and weed-free (i.e., weeded) plots or bands of weeds growing only in row (IR). The null hypothesis of this research was that field corn yields would be ranked as weed-free > BR weedy only > IR weedy only > weedy (IR + BR weedy) in response to season-long weed interference by these four treatments. Weeds growing as bands closest to field corn were expected to reduce field corn yields more than those growing as bands further away between field corn rows. Field corn yield response to these four weed interference treatments was studied in Missouri for 4 yr. In late summer, most weed ground cover consisted of giant foxtail, the chief weed present, and common waterhemp, a lesser weed. Observed field corn yields averaged for 4 yr were ranked as weed-free > IR weedy only > BR weedy only > weedy. Field corn yields of the IR weedy only, BR weedy only, and weedy treatments averaged 76, 63, and 41%, respectively, of the weed-free treatment (=7,820 kg/ha). In two of the 4 yr, field corn yield of the IR weedy treatment exceeded that of the BR weedy treatment, whereas these treatments could not be statistically distinguished from one another in the other 2 yr. These research results refute the null hypothesis and demonstrate that it may be more critical to control BR than IR weeds, although controlling both BR and IR weeds maximized field corn yields.

Keywords

Common waterhemp, Amaranthus rudus Sauer #3 AMATAgiant foxtail, Setaria faberii Herrm. SETFAcorn, Zea mays L. ‘Pioneer 3379’ and ‘Pioneer 33G28’BandingcompetitioninterferenceBR, between rowIR, in row
Type
Research
Information
Weed Technology , Volume 17 , Issue 4 , December 2003 , pp. 755 - 763
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Anderson, R. L. 1997. Longspine sandbur (Cenchurs longispinus) ecology and interference in irrigated corn (Zea mays). Weed Technol. 11:667–671.Google Scholar
Ascard, J. 1994. Dose-response models for flame weeding in relation to plant size and density. Weed Res. 34:377–385.Google Scholar
Ascard, J. 1995. Effects of flame weeding on weed species at different developmental stages. Weed Res. 35:397–411.Google Scholar
Ascard, J. 1997. Flame weeding: effects of fuel pressure and tandem burners. Weed Res. 37:77–86.Google Scholar
Beckett, T. H., Stoller, E. W., and Wax, L. M. 1988. Interference of four annual weeds in corn (Zea mays). Weed Sci. 36:764–769.CrossRefGoogle Scholar
Bhowmik, P. C. and Doll, J. D. 1984. Allelopathic effects of annual weed residues on growth and nutrient uptake of corn and soybeans. Agron. J. 76:383–388.Google Scholar
Bosnic, A. C. and Swanton, C. J. 1997. Influence of barnyardgrass (Echinochloa crus-galli) time of emergence and density on corn (Zea mays). Weed Sci. 45:276–282.Google Scholar
Bowman, G. ed. 1997. Steel in the Field. A Farmer's Guide to Weed Management Tools. Beltsville, MD: Sustainable Agriculture Network, National Agricultural Library.Google Scholar
Buhler, D. D. 1997. Effects of tillage and light environment on emergence of 13 annual weeds. Weed Technol. 11:496–501.Google Scholar
Donald, W. W. 1998a. Estimated soybean (Glycine max) yield loss from herbicide damage using ground cover or rated stunting. Weed Sci. 46:454–458.Google Scholar
Donald, W. W. 1998b. Estimating relative crop yield loss resulting from herbicide damage using crop ground cover or rated stunting, with maize and sethoxydim as a case study. Weed Res. 38:425–431.Google Scholar
Donald, W. W. 2000a. Alternative ways to control weeds between rows in weeded check plots in corn (Zea mays) and soybean (Glycine max). Weed Technol. 14:36–44.CrossRefGoogle Scholar
Donald, W. W. 2000b. Between-row mowing + in-row band-applied herbicide for weed control in Glycine max . Weed Sci. 48:487–500.Google Scholar
Donald, W. W. 2000c. Timing and frequency of between-row mowing and band-applied herbicide for annual weed control in soybean. Agron. J. 92:1013–1019.Google Scholar
Donald, W. W. 2002. Glyphosate effects on ground cover of tall fescue waterways and estimated soil erosion. J. Soil Water Conserv 57:237–243.Google Scholar
Donald, W. W., Johnson, W. G., Nelson, K., and Archer, D. W. 2003. In row and between row zone herbicide application at different rates controls annual weeds and reduces total residual herbicide use in corn (Zea mays). WSSA Abst. 43:68.Google Scholar
Drew, J. S. and Van Arsdall, R. N. 1966. The economics of preemergence herbicides for controlling grass weeds in corn production. Ill. Agric. Econ 6:25–30.Google Scholar
Echtenkamp, G. W. and Moomaw, R. S. 1989. No-till corn production in a living mulch system. Weed Technol. 3:261–266.Google Scholar
Fausey, J. C., Kells, J. J., Swinton, S. M., and Renner, K. A. 1997. Giant foxtail (Setaria faberi) interference in nonirrigated corn (Zea mays). Weed Sci. 45:256–260.CrossRefGoogle Scholar
Fiebig, W. W., Shilling, D. G., and Knauft, D. A. 1991. Peanut genotype response to interference from common cocklebur. Crop Sci 31:1289–1292.Google Scholar
Fogelberg, F. 1999. Night-time soil cultivation and intra-row brush weeding for weed control in carrots (Daucus carota L). Biol. Agric. Hortic 17:31–45.Google Scholar
Fogelberg, F. and Gustavsson, A. M. D. 1999. Mechanical damage to annual weeds and carrots by in-row brush weeding. Weed Res. 39:469–479.Google Scholar
Fogelberg, F. and Kritz, G. 1999. Intra-row weeding with brushes on vertical axes—factors influencing in-row soil height. Soil Tillage Res 50:149–157.CrossRefGoogle Scholar
Ford, G. T. and Pleasant, J. M. 1994. Competitive abilities of six corn (Zea mays) hybrids with four weed control practices. Weed Technol. 8:124–128.Google Scholar
Ghosheh, H. Z., Hoschouser, D. L., and Chandler, J. M. 1996. The critical period of johnsongrass (Sorghum halepense) control in field corn (Zea mays). Weed Sci. 44:944–947.Google Scholar
Halford, C., Hamill, A. S., Zhang, J., and Doucet, C. 2001. Critical period of weed control in no-till soybean (Glycine max) and corn (Zea mays). Weed Technol. 15:737–744.Google Scholar
Hall, M. R., Swanton, C. J., and Anderson, G. W. 1992. The critical period of weed control in grain corn (Zea mays). Weed Sci. 40:441–447.Google Scholar
Henry, W. T. and Bauman, T. T. 1989. Interference between soybeans (Glycine max) and common cocklebur (Xanthium strumarium) under Indiana field conditions. Weed Sci. 37:753–760.Google Scholar
Hoshmand, A. R. 1994. Experimental Research Design and Analysis. A Practical Approach for Agricultural and Natural Sciences. Boca Raton, FL: CRC.Google Scholar
Knake, E. L. and Slife, F. W. 1962. Competition of Setaria faberii with corn and soybeans. Weeds 10:26–29.Google Scholar
Knake, E. L. and Slife, F. W. 1965. Giant foxtail seeded at various times in corn and soybeans. Weeds 13:331–334.Google Scholar
Knezevic, S. Z., Evans, S. P., Blankenship, E. E., Van Acker, R. C., and Lindquist, J. L. 2002. Critical period for weed control: the concept and data analysis. Weed Sci. 50:773–786.Google Scholar
Knezevic, S. Z., Weise, S. F., and Swanton, C. J. 1994. Interference of redroot pigweed (Amaranthus retroflexus) in corn (Zea mays). Weed Sci. 42:568–573.CrossRefGoogle Scholar
Knezevic, S. Z., Weiss, S. F., and Swanton, C. J. 1995. Comparison of empirical models depicting density of Amaranthus retroflexus L. and relative leaf area as predictors of yield loss in maize (Zea mays L.). Weed Res 35:207–214.Google Scholar
Lindquist, J. L., Mortensen, D. A., and Westra, P. et al. 1999. Stability of corn (Zea mays)-foxtail (Setaria spp.) interference relationships. Weed Sci. 47:195–200.Google Scholar
Martin, R. C., Greyson, P. R., and Gordon, R. 1999. Competition between corn and a living mulch. Can. J. Plant Sci 79:579–586.Google Scholar
Mickelson, J. A. and Harvey, R. G. 1999. Effects of Eriochloa villosa density and time of emergence on growth and seed production in Zea mays . Weed Sci. 47:687–692.Google Scholar
Mohler, C. L. 1991. Effects of tillage and mulch on weed biomass and sweet corn yield. Weed Technol. 5:545–552.Google Scholar
Mulder, T. A. and Doll, J. D. 1993. Integrating reduced herbicide use with mechanical weeding in corn (Zea mays). Weed Technol. 7:382–389.Google Scholar
Ngouajio, M., Lemieux, C., and Leroux, G. D. 1999a. Prediction of corn (Zea mays) yield loss from early observations of the relative leaf area and the relative leaf cover of weeds. Weed Sci. 47:297–304.CrossRefGoogle Scholar
Ngouajio, M., Leroux, G. D., and Lemieux, C. 1999b. Influence of images recording height and crop growth stage on leaf cover estimates and their performance in yield prediction models. Crop Prot 18:501–508.Google Scholar
Ngouajio, M., Leroux, G. D., and Lemieux, C. 1999c. A flexible sigmoidal model relating crop yield to weed relative leaf cover and its comparison with nested models. Weed Res. 39:329–343.CrossRefGoogle Scholar
Padgitt, M., Newton, D., Penn, R., and Sandretto, C. 2000. Production Practices for Major Crops in U. S. Agriculture, 1990–1997. Resource Economics Division, Economic Research Service, U. S. Department of Agriculture, Statistical Bulletin NO. 969. 104 p.Google Scholar
Pike, D. R., Stoller, E. W., and Wax, L. M. 1990. Modeling soybean growth and canopy apportionment in weed-soybean (Glycine max) competition. Weed Sci. 38:522–527.CrossRefGoogle Scholar
Rajcan, I. and Swanton, C. J. 2001. Understanding maize-weed competition: resource competition, light quality and the whole plant. Field Crops Res 71:139–150.Google Scholar
Raynal, D. J. and Bazzaz, F. A. 1975. The contrasting life-cycle strategies of three summer annuals found in abandoned fields in Illinois. J. Ecol 63:587–596.Google Scholar
Rikoon, J. S., Constance, D. H., and Galetta, S. 1996. Factors affecting farmer's use and rejection of banded pesticide applications. J. Soil Water Conserv 51:322–329.Google Scholar
Ruiz, J. A., Sanchez, J. J., and Goodman, M. M. 1998. Base temperature and heat unit requirement of 49 Mexican maize races. Maydica 43:277–282.Google Scholar
Santelmann, P. W., Meade, J. A., and Peters, R. A. 1963. Growth and development of yellow foxtail and giant foxtail. Weeds 11:139–142.Google Scholar
Schrieber, M. M. 1965. Development of giant foxtail under several temperatures and photoperiods. Weeds 13:40–44.Google Scholar
Schroder, D., Hatley, J. C., and Finley, R. M. 1984. The contribution of herbicides and other technologies to corn production in the corn belt region, 1964 to 1979. N. Cent. J. Agric. Econ 6:95–104.Google Scholar
Smith, M. A. and Carter, P. R. 1997. Strip intercropping corn and alfalfa. J. Prod. Agric 10:345–353.Google Scholar
SPSS. 2001. SPSS Base 11 User's Guide and SPSS Applications Guide. Chicago, IL: SPSS.Google Scholar
SPSS Science. 1999. Sigma Scan Pro. Version 5. Chicago, IL: SPSS.Google Scholar
Stoller, E. W., Harrison, S. K., Wax, L. M., Regnier, E. E., and Nafziger, E. D. 1987. Weed interference in soybeans (Glycine max). Rev. Weed Sci 3:155–182.Google Scholar
Zemenchik, R. A., Albrecht, K. A., Boerboom, C. M., and Lauer, J. G. 2000. Corn production with kura clover as a living mulch. Agron. J. 92:698–705.Google Scholar
Zimdahl, R. L. 1980. Weed-Crop Competition, a Review. Corvallis, OR: Oregon State University. Pp. 46–49, 84–85.Google Scholar